Lane changes are complex driving behaviors and frequently involve safety-critical situations. This study aims to develop a lane-change-related evasive behavior model, which can facilitate the development of safety-aware traffic simulations and predictive collision avoidance systems. Large-scale connected vehicle data from the Safety Pilot Model Deployment (SPMD) program were used for this study. A new surrogate safety measure, two-dimensional time-to-collision (2D-TTC), was proposed to identify the safety-critical situations during lane changes. The validity of 2D-TTC was confirmed by showing a high correlation between the detected conflict risks and the archived crashes. A deep deterministic policy gradient (DDPG) algorithm, which could learn the sequential decision-making process over continuous action spaces, was used to model the evasive behaviors in the identified safety-critical situations. The results showed the superiority of the proposed model in replicating both the longitudinal and lateral evasive behaviors.

本研究旨在开发与车道变更相关的闪避行为模型，以便为安全感知交通模拟和预测性碰撞避免系统的发展提供帮助。本研究利用了大规模的连通车辆数据，使用新的代理安全度量2D-TTC确定了车道变更过程中的安全关键情况，使用深度确定性策略梯度（DDPG）算法进行建模，结果表明该模型在复制纵向和横向闪避行为方面具有优越性。

安全关键车道变更中司机规避行为的建模：二维时间碰撞和深度强化学习